S&P 500

Clase series de tiempo, primavera 2024

Descarga de datos

library(tidyquant)
library(tidyverse)
library(tsibble)
library(fable)
library(feasts)
data = tq_get("^GSPC", get="stock.prices", from = "2022-01-01", to = "2024-03-28") %>% select(date, adjusted) %>% as_tsibble(index = date)

data

Row Number

data_row_number <- data |>
  mutate(t = row_number()) |>
  update_tsibble(index = t, regular = TRUE) %>% select(t, adjusted)

data_row_number

data_row_number |>
  model(
    STL(adjusted ~ season(period = 5) + season(period = 20) + season(period = 252),
        robust = TRUE)
  ) |>
  components() |>
  autoplot() + labs(x = "Observation")

my_dcmp_spec <- decomposition_model(
  STL(adjusted ~ season(period = 5),
      robust = TRUE),
  ETS(season_adjust)
)
fc <- data_row_number |>
  model(my_dcmp_spec) |>
  forecast(h = 25)

plot = fc |>
  fill_gaps() |>
  autoplot(data_row_number) +
  labs(y = "SP500",
       title = "SP500")

plot

fit3 <- data_row_number %>% 
  model(
    harmonic = ARIMA(adjusted ~ trend() + fourier(period = 5, K = 2) + fourier(period = 25, K = 4) + fourier(period = 252, K = 10))
  )

fc <- fit3 %>%
  forecast(h = 25)

fc |>
  fill_gaps() |>
  autoplot(data_row_number) +
  labs(y = "SP500",
       title = "SP500")

Fill na

data

data_fill_na = fill_gaps(data) %>% fill(adjusted, .direction = "down")
data_fill_na

comp_stl <- data_fill_na %>% 
  model(
    STL(adjusted, robust = TRUE)
  ) %>% 
  components() 

comp_stl %>% 
  autoplot()

comp_stl %>% 
  autoplot(season_year)

comp_stl %>% 
  autoplot(season_week)

comp_stl %>% 
  ggplot(aes(x = date, y = season_adjust)) +
  geom_line()

my_dcmp_spec <- decomposition_model(
  STL(adjusted ~ season(period = 5),
      robust = TRUE),
  ETS(season_adjust)
)
fc <- data_fill_na |>
  model(my_dcmp_spec) |>
  forecast(h = 20)

plot = fc |>
  fill_gaps() |>
  autoplot(data_fill_na) +
  labs(y = "SP500",
       title = "SP500")

plot

fit3 <- data_fill_na %>% 
  model(
    harmonic = ARIMA(adjusted ~ trend() + fourier(period = 5, K = 2) + fourier(period = 252, K = 20))
  )

Warning: NaNs produced

fc <- fit3 %>%
  forecast(h = 25)

fc |>
  fill_gaps() |>
  autoplot(data_fill_na) +
  labs(y = "SP500",
       title = "SP500")

fit3 <- data_fill_na %>% 
  model(
    harmonic = ARIMA(adjusted ~ trend() + fourier(period = "week", K = 2) +
          fourier(period = "year", K = 3))

  )

fc <- fit3 %>%
  forecast(h = 25)

fc |>
  fill_gaps() |>
  autoplot(data_fill_na) +
  labs(y = "SP500",
       title = "SP500")

Prophet

library(fable.prophet)
colnames(data) = c("ds", "y")

fit_prophet = data %>% model(prophet = prophet(y))

fc <- fit_prophet %>% forecast(h = 25)
fc %>% autoplot(data)

fit_prophet |>
  components() |>
  autoplot()

components(fit_prophet)

fit_prophet = data %>% model(prophet(y ~ season(type = "additive")))

fc <- fit_prophet %>% forecast(h = 25)
fc %>% autoplot(data)

fit_prophet |>
  components() |>
  autoplot()

fit_prophet = data %>% model(prophet(y ~ season(period = "day", order = 5) +
                                        season(period = "week", order = 2) +
                                        season(period = "year", order = 2)))

fc <- fit_prophet %>% forecast(h = 25)
fc %>% autoplot(data)

fit_prophet |>
  components() |>
  autoplot()

?prophet

index = tq_index("SP500") %>% select(symbol, weight)

Getting holdings for SP500

index = index %>% filter(symbol != "-")
index

sum(index$weight)

[1] 0.9962656

data = tq_get(index$symbol, get = "stock.prices", from = "2024-01-01", to = "2024-03-29") %>% select(symbol, date, adjusted)